Under a normal condition, a complete face recognition system consists of four parts, i.e. face detection, eye location, feature extraction and face recognition. In a recognition process, no matter whether a global feature or local feature of a face is utilized or a geometric feature or an algebraic feature of an image is adopted, a change in an orientation of the face may greatly influence a recognition result, and thus, normalization processing on the image is required before recognition. A condition for image normalization includes positions of eyes, for specific reasons as follows:
1) a distance between centres of the eyes is minimally influenced by light and expression changes, and a direction of a connecting line of the centres of the eyes is deflected along with turning of the face, and may be used as a basis for image rotation; and
2) each extracted feature value is normalized by a distance between the eyes, and then these feature values are invariable in translation, rotation and scale.
On the premise that a position of a face has been detected, whether eyes are accurately located or not directly influences a feature extraction effect to further influence face recognition accuracy, so that current scholars locate accuracy of eyes as a key and bottleneck of development of face recognition to practicability.
Early face recognition algorithms are all implemented on the premise that coordinates of eyes are supposed to be accurately located. In the last few decades, researches on eye location methods have been greatly developed, and researchers at home and abroad proposed some eye location methods, which may substantially be divided into the following five types:
(1) A Prior-Rule-Based Method:
the prior-rule-based method is also called a knowledge-based method, and mainly summarizes features of a face into some simple rules, these rules reflect relationships between a peculiar attribute of the face and each facial feature of the face, and for example, the face is central symmetric. Candidate points or areas of the eyes may be approximately determined according to these relationships, and then corresponding processing is performed.
The prior-rule-based method is simple and rapid, but is only effective for a face image with a simpler background and regular facial features. For accurately locating the eyes, other methods for eliminating interference, such as eyebrows, eyeglasses and eyelashes, are also required.
(2) A Geometric-Information-Based Method:
since positions and distances of each organ on the face are relatively fixed, a geometric model of the face may be established according to a distribution rule of the organs on the face, and then positions of each facial feature point are matched in the face image.
The geometric-information-based method has higher requirements on template selection and model construction, and a great amount of geometric position information of the face is utilized, which may cause a poor effect of location under a complex condition. Therefore, an existing face recognition system usually implements location by another method under the assistance of the geometric-information-based method.
(3) A Skin Colour Information-Based Method:
colour information has been more and more applied to the field of researches on computer vision. A skin colour is important information of the face, is completely independent of a detailed feature of the face, is applicable to a change condition of rotation, an expression, a pose and the like, and is relatively stable. The skin colour is different from colours of most of background objects to a certain extent, and utilizing the skin colour may reduce eye searching time and reduce interference of a complex background to eye location.
The skin colour information-based method is simple and rapid, but is easily influenced by an external light source, image acquisition equipment and the like, and may cause phenomena of light reflection of the face image, image colour abnormity and the like to cause incapability of the system in better distinguishing skin colour and non-skin colour pixels. Moreover, skin colour information may only be configured for a colour image, may not be configured for processing of a greyscale image, and is not so universal.
(4) A Statistical-Information-Based Method:
the statistical-information-based method usually obtains a group of training classifiers by training and learning of a great number of target samples and non-target samples, and then performs target detection according to these classifiers.
The statistical-information-based method for eye location is greatly improved in accuracy, but a great number of samples are required to be manufactured in advance for training, a training process is complex, and a period is long.
(5) An Associated-Information-Based Method:
the associated-information-based method continuously narrows a range of the candidate points of the eyes by relative position data of other organs on the face, thereby implementing eye location.
The associated-information-based method utilizes a restriction relationship among feature points of the face, is improved in location accuracy along with increase of restriction conditions, and is well adapted to a complex background, face pose and expression change and the like, but at the same time of improving the accuracy, operation of an algorithm is also increased, and a location speed is reduced.
However, the eye location methods in a related technology all have similar defects: 1) states of the eyes are different when the eyes are opened and closed, the eyes in an image where the eyes are opened are easy to locate because the image includes pupils, only a “black line” is formed in an image where the eyes are closed, positions of the eyes in the images in the two states have different feature and greyscale distribution, it is difficult to find common features if the same methods are adopted for location, for example, template matching, adoption of the same methods for location may easily cause false location, the existing eye location methods hardly ever classify the eye states, and different location methods should be adopted for different eye states; and 2) most of the existing eye location methods only purely locate the eyes on the face, but actually, eye location may also be interfered by eyebrows, canthi and the like sometimes, and although the eyebrows or the canthi are located, such an obvious error may not be corrected.
For the problem of poorer eye location accuracy in the related technology, there is yet no effective solution.